Dynamic chain-based thresholding using global characteristics

ABSTRACT

One embodiment relates to an apparatus for image processing. The apparatus includes a candidate edge chain identifier for identifying candidate edge chains in an image being processed, means for calculating a dynamic chain-based threshold function that is dependent on at least one characteristic of the image being processed, and a threshold applicator for applying the dynamic chain-based threshold function to the candidate edge chains. The characteristic of the image being processed may be global in that it is determined from the overall image being processed. A system may include an encoder or a decoder, both of which include the above apparatus. Another embodiment relates to a method for image processing. The method determines a dynamic chain-based threshold function that is dependent on at least one characteristic of the image being processed and applies the dynamic threshold to a candidate edge chain.

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This patent application claims the benefit of co-pendingProvisional Application No. 60/272,332, filed Feb. 28, 2001, andentitled “Dynamic Thresholding,” the disclosure of which is incorporatedherein by reference. This patent application is related to U.S. patentapplication Ser. No. 09/550,705, filed Apr. 17, 2000 and entitled“Method and Apparatus for Efficient Video Processing,” the disclosure ofwhich is incorporated herein by reference. This invention is related toU.S. patent application Ser. No. 09/591,438, filed Jun. 9, 2000 andentitled “Method and Apparatus for Digital Image Segmentation,” thedisclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

[0002] The present invention relates in general to image processing.More particularly, it may be used in relation to, for example, segment-or object-based image processing.

BACKGROUND OF THE INVENTION

[0003] It is often desirable to identify and delineate segments of animage. Image segmentation is the process of partitioning an image into aset of non-overlapping parts, or segments, that together constitute theentire image. For example, the segmentation may divide the image into abackground segment and a plurality of foreground segments. A foregroundsegment may, for example, correspond to an object, or a part of anobject, in the image. Digital image or video processing may befacilitated by image segmentation.

[0004] In order to identify and delineate the segments in an image, theboundaries or edges of the segments may be detected or extracted.Extracting those edges or boundaries between segments using anelectronically-implementable edge-detection algorithm or routine isdifficult to do accurately for a general case. Such an algorithm mustwork for a wide range of images with varying amounts of scales anddetails. To be suitable for applications requiring fully-automatedsegmentation, the algorithm should not require user guidance or priorknowledge of the image to be segmented.

[0005] One technique for edge extraction is referred to as Canny edgedetection. The process of conventional Canny edge detection begins withsmoothing of intensity values by convolution with a two-dimensionalGaussian function. The smoothed image data is then differentiated tocalculate a gradient vector function indicating the rate and directionof intensity change at each point in the image. Then, a non-maximasuppression process determines candidate edge points by eliminatingnon-maxima points perpendicular to edge directions. Subsequently,point-based thresholding is applied to the candidate edge points. InCanny edge detection, the point-based thresholding uses ahysteresis-type (two level) threshold function. Hysteresis-typethreshold function evaluates the non-maxima points using two thresholdlevels. If a gradient value at a point lies above the upper thresholdlevel, then the point is automatically accepted. If the gradient valuelies below the lower threshold level, then the point is automaticallyrejected. If the gradient value lies in between the upper and lowerthresholds, then the point is accepted if it is connected to a highvalue point. In other words, a chain does not stop until a point isreached that has a gradient value below the lower threshold.

[0006] Despite the accomplishments of prior edge extraction techniques,problems remain to be overcome in order to improve the edge extraction.One problem to be overcome to improve edge extraction is theover-identification of edges (i.e., the extraction of too many edges).For example, consider one white shirt on a black background. It may bedesired to identify the white shirt as an image segment with a singleedge around its boundary separating it from the black backgroundsegment. However, folds in the shirt may produce shadowed areas that maycause additional edges to be generated by the edge-detection routine.These extraneous edges may result in the creation of additional segmentswithin the otherwise plain white shirt, or they may be superfluous inthat they cannot be connected to enclose a segment.

SUMMARY OF THE INVENTION

[0007] One embodiment relates to an apparatus for image processing. Theapparatus includes a candidate edge chain identifier for identifyingcandidate edge chains in an image being processed, means for calculatinga dynamic chain-based threshold function that is dependent on at leastone characteristic of the image being processed, and a thresholdapplicator for applying the dynamic chain-based threshold function tothe candidate edge chains. The characteristic of the image beingprocessed may be global in that it is determined from the overall imagebeing processed.

[0008] A system may include an encoder or a decoder, both of whichinclude the above apparatus. Another embodiment relates to a method forimage processing. The method determines a dynamic chain-based thresholdfunction that is dependent on at least one characteristic of the imagebeing processed and applies the dynamic threshold to a candidate edgechain.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 is a flow chart depicting a conventional edge-detectionprocess.

[0010]FIG. 2 is a flow chart depicting an edge-detection process usingdynamic chain-based thresholding dependent on global characteristics ofan image in accordance with an embodiment of the invention.

[0011]FIG. 3 is a flow chart depicting an edge-detection process usingdynamic chain-based thresholding dependent on local characteristics ofan image in accordance with an embodiment of the invention.

[0012]FIG. 4 is a diagram depicting a dynamic chain-based thresholdingapparatus in accordance with an embodiment of the invention.

[0013]FIG. 5 is a diagram depicting a system in accordance with anembodiment of the invention.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

[0014] One technique for dealing with the over-identification of edgesrelates to applying chain-based thresholding to the candidate edgechains. As the terminology is used in the present specification,chain-based thresholding differs from point-based thresholding. Inpoint-based thresholding, a threshold function is applied to a point. Inchain-based thresholding, a threshold function is applied to a chain ofpoints. Chain-based thresholding finds undesired or extraneous chains sothat they may be eliminated from consideration. Candidate edge chainsthat do not meet the threshold requirements (i.e., that do not pass thethreshold function) would be removed from the set of identified edgechains. For example, those candidate edge chains that are particularlyshort may not pass the threshold function and so may be removed asextraneous.

[0015] Previous known techniques have used static threshold functionsfor chain-based thresholding. However, such static threshold functionsmay categorize too many or too few chains as extraneous chains,depending on the threshold chosen and the particular image beingprocessed. In other words, a static threshold function for chain-basedthresholding is likely to have difficulty working well across a widerange of images.

[0016] The present patent application describes the advantageousimprovement of using automatic dynamic threshold functions forchain-based thresholding. Such dynamic threshold functions are dependentupon characteristics of the image being processed. In contrast, staticthreshold functions do not take into account characteristics of a givenimage. Alternatively, the thresholds may be selected by the human userfor a particular image. In other words, dynamic chain-based thresholdingautomatically determines whether or not to retain a candidate edge chaindepending, not only upon the characteristics of the chain in question,but also upon characteristics of the image in which the chain resides.

[0017] In accordance with one embodiment of the invention, thechain-based thresholding may be dependent on one or more dynamicglobally-measured characteristics of the image. The dynamicglobally-measured characteristic is one that is measured over an entireimage frame or over substantially all of the image frame. For example,the dynamic globally-measured characteristic may be a measure of colorvariation in the image frame. The measure may be, for example, a mean ora median of color variation in the frame.

[0018] In accordance with another embodiment of the invention, thechain-based thresholding may be dependent on one or more dynamiclocally-scaled characteristics of the image. For example, the dynamiclocally-scaled characteristic may be measured within a localneighborhood around points in the chain being thresholded. Thecharacteristic may be, for example, related to the chain strength, andthe local scaling may depend on the local extrema strength or densitysurrounding each point in the chain.

[0019]FIG. 1 is a flow chart depicting a conventional edge-detectionprocess. The process 100 as depicted includes nine steps that aredescribed below.

[0020] In a first step 102, parameters are set for a point-basedthreshold function. For example, the point-based threshold function maybe a single-level threshold or a hysteresis-type threshold. In the caseof a hysteresis-type threshold, the parameters that are set may includevalues for the upper and lower threshold levels. Typically, the upperthreshold level may be set to be two to three times the lower thresholdlevel. In a second step 104, parameters are set for a chain-basedthreshold function. For example, the chain-based threshold function maybe set to eliminate chains less than n points in length and/or be set toeliminate chains where the sum of the gradient values of its maxima isless than a certain threshhold. In this conventional process 100depicted in FIG. 1, both the first and second steps 102 and 104 needonly be performed once and may be done long before the other steps. Thisis because the point-based and chain-based threshold functions are bothstatic threshold functions. Static threshold functions do not changefrom image to image. In other words, a static threshold function isindependent of the image characteristics.

[0021] In a third step 106, the processing of an image begins by inputof the image data. The image data typically comprises pixel colorvalues. Pixel color values can be selected from any number of pixelcolor spaces. One color space in common use is known as the YUV colorspace, wherein a pixel color value is described by the triple (Y, U, V),where the Y component refers to a grayscale intensity or luminance, andU and V refer to two chrominance components. The YUV color space iscommonly seen in television applications. Another common color space isreferred to as the RGB color space, wherein R, G and B refer to the Red,Green and Blue color components, respectively. The RGB color space iscommonly seen in computer graphics representations, along with CYMB(cyan, yellow, magenta and black) often used with computer printers.

[0022] In a fourth step 108, candidate edge points (also called“edgels”) in the image are identified. This may be accomplished usingvarious methods, such as gradient-type methods and second-order methods.For example, as described above, the Canny edge detection methodidentifies candidate edge points uses a gradient-type method andnon-maxima suppression. In a fifth step 110, a point-based threshold maybe applied to the candidate edge points. For example, the point-basedthreshold may be a single-level threshold or a hysteresis-typethreshold. The Canny edge detection method uses a hysteresis-typethreshold. In a sixth step 112, the candidate edge points that do notpass the point-based threshold are removed from the set of edge points.

[0023] In a seventh step 114, those edge points that passed thepoint-based threshold are linked to form candidate edge chains. Thisstep involves grouping together edge points to form the edge chains. Thelinking may be accomplished, for example, by considering each edgepoint's relationship to neighboring edge points (local edge linking) orby consider all the edge points at once and grouping those edge pointsthat match a similarity constraint such as sharing a same edge equation(global edge linking). In an eighth step 116, a chain-based thresholdmay be applied to the candidate edge chains. For example, thechain-based threshold may be a single-level threshold based upon achain's length. In a ninth step 118, the candidate edge chains that donot pass the chain-based threshold are removed from the set of edgechains. The result is the set of edge chains detected by theedge-detection process 100 for that image. After the ninth step 118, theprocess 100 loops back to the third step 106 and image data is input fora next image.

[0024]FIG. 2 is a flow chart depicting an edge-detection process usingdynamic chain-based thresholding dependent on one or more globalcharacteristics of an image in accordance with an embodiment of theinvention. A globally-measured characteristic is one that is measuredover an entire image frame or over substantially all of the image frame.The process 200 as depicted includes ten steps that are described below.

[0025] The first step 102 may be the same as in the conventional process100. In this step 102, the parameters are set for a point-basedthreshold function. For example, the point-based threshold function maybe a single-level threshold or a hysteresis-type threshold. However, thesecond step 104 from the conventional process 100 may be missing fromthis process 200. This is because the chain-based threshold function isdynamically determined based on one or more characteristic(s) of theimage being processed, and so parameters for the chain-based thresholdfunction cannot yet be set in this process 200.

[0026] The second 106, third 108, fourth 110, fifth 112, and sixth 114steps in this process 200 may be the same as or similar to thecorresponding steps (106, 108, 110, 112, and 114) in the conventionalprocess 100. These steps involve inputting the image data (step 106),identifying candidate edge points in the image (step 108), applying thepoint-based threshold to the candidate edge points (step 110), removingthose points that do not pass the point-based threshold (step 112), andlinking the edge points to determine candidate edge chains (step 114).

[0027] The other steps (202, 204, 206, and 208) in this process 200 aredistinctive from the conventional process 100. These steps relate to thedetermination and application of a dynamic chain-based threshold that isdependent on one or more global characteristics of the image beingprocessed.

[0028] In the first distinctive step 202, a calculation is made of oneor more globally-measured characteristic(s) of the image beingprocessed. This step 202 can be done subsequent to the image data inputstep 106. In accordance with one embodiment of the invention, aglobally-measured characteristic may be a measure of color variation inthe image frame. The measure may be, for example, a mean or a median ofcolor variation in the frame.

[0029] Color in an image may be represented for each pixel by thepixel's color values. In a monochromatic image, the pixel color valuecan be represented by a single number. More commonly, in polychromaticcolor images, a pixel's color value comprises three or four colorcomponents and the color value in a polychromatic color image can bethought of as a vector in a “color space” having as many dimensions asthere are components to the color values. For example, the color valuesof an image might be representable as vectors in the YUV space. In theYUV color space, the Y component represents luminance, or intensity, andthe U and V components represent chrominance. Typically, the Y componentis often of greater magnitude than the U or V components.

[0030] In accordance with embodiments of the present invention, thevariation of each color component may be calculated globally overpixels, or edge points, or edge chains in a frame. The calculation maybe of a mean or median value of the variation of the color component.The component variations may then be combined (by summation of squares)into a single global color variation mean or median value. Thecombination may weight the components differently. For example, in YUVspace, the U and V components may be weighted more heavily (for example,four times more) than the Y component, in order to more stronglyrepresent the contributions of the U and V components.

[0031] In the second distinctive step 204, the dynamic chain-basedthreshold is determined using the one or more global characteristic(s)calculated in the first distinctive step 202. In one embodiment, aframe-wide histogram of chain strengths is determined, where the chainstrength is an integrated intensity (integration of gradient values)over edge points comprising the chain. The dynamic chain-based thresholdmay then be chosen as a percentage point on the histogram such that apercentage of the chains are below the threshold. The dynamicchain-based threshold may, for example, be linearly related to themeasure of global color variation; for example, a fraction “a” of themeasure of global color variation, where 0≦a<1 (“a” is greater than zeroand less than or equal to one). Hence, the lower the global colorvariation, the more significant chain strength measures become, and so alower threshold results. On the other hand, the higher the global colorvariation the less significant chain strength measures become, and so ahigher threshold results. Of course, other dependencies of the dynamicchain-based threshold on the global characteristic(s) may also be used.

[0032] In the third distinctive step 206, the dynamic chain-basedthreshold from the second distinctive step 204 is applied to thecandidate edge chains. Note that this step 206 is dependent not only onthe linking step 114, but also on the dynamic threshold determinationstep 204. In one example, the dynamic chain-based threshold may be athreshold based upon a chain's strength, where the threshold strengthmay depend on the calculated global characteristic(s) as describedabove. Chains of strength at or above the threshold strength may pass,while those of strength below the threshold strength may fail.

[0033] Of course, besides a chain's strength, other features of a chainmay be used as the basis for determination and application of a dynamicchain-based threshold. For example, a chain's length may be used,instead of a chain's strength.

[0034] In the fourth distinctive step 208, those candidate edge chainsare removed from the set of edge chains that do not pass the dynamicchain-based threshold applied in the third distinctive step 206. Theresult is the set of edge chains detected by this edge-detection process200 for that image. After that step 208, the process 200 loops back tostep 106 and image data is input for a next image.

[0035]FIG. 3 is a flow chart depicting an edge-detection process usingdynamic chain-based thresholding dependent on one or morelocally-measured characteristic(s) of an image in accordance with anembodiment of the invention. A locally-measured characteristic may be acharacteristic that is measured within a local vicinity of edge pointsin the chain being thresholded.

[0036] The first step 102 may be the same as in the conventional process100. In this step 102, the parameters are set for a point-basedthreshold function. For example, the point-based threshold function maybe a single-level threshold or a hysteresis-type threshold. However,similarly as for the second process 200, the second step 104 from theconventional process 100 may be missing from this third process 300.This is because the chain-based threshold function is dynamicallydetermined based on one or more characteristic(s) of the image beingprocessed, and so parameters for the chain-based threshold functioncannot yet be set in this process 300.

[0037] Similarly as for the second process 200, the second 106, third108, fourth 110, fifth 112, and sixth 114 steps in this third process300 may be the same as or similar to the corresponding steps (106, 108,110, 112, and 114) in the conventional process 100. These steps involveinputting the image data (step 106), identifying candidate edge pointsin the image (step 108), applying the point-based threshold to thecandidate edge points (step 110), removing those points that do not passthe point-based threshold (step 112), and linking the edge points todetermine candidate edge chains (step 114).

[0038] The next two steps (302and 304) in this process 300 aredistinctive from the conventional process 100. These steps relate to thedetermination and application of a dynamic chain-based threshold that isdependent on one or more local characteristics around points in the edgechain being considered.

[0039] In the first distinctive step 302, a calculation is made of oneor more locally-scaled characteristic(s) of the chain being considered.This calculation step 304 may be done on a per chain basis and maydepend on the local environment around the points in the chain. In oneembodiment, the locally-scaled characteristic may be the chain strength.For example, a gradient value at each edge point in the chain may bescaled based on the strength and/or density of local extrema in thevicinity of the point. In one specific embodiment, a neighborhood withina 15-pixel radius of an edge point is considered, though larger orsmaller neighborhoods of varying shapes may also be considered.

[0040] The greater the strength and/or density around an edge point, theless significant or important that edge point becomes, and so the morescaled down the value at the edge point becomes. In particular, a firstpoint at one end of a chain may be surrounded with a higher extremastrength and/or density than a second point at another end of the chainis. In that case, the gradient value at the first point would be moresubstantially scaled down (and hence reduced in significance) incomparison to the gradient value at the second point.

[0041] In one specific embodiment, the scaling function “F” for point“i” may be in accordance with Equation 1. Equation 1 breaks down thescaling function into two factors. The first factor, F1(i), is relatedto local intensity (strength), and the second factor F2(i) is related tolocal density. The local intensity refers to the intensity of theextrema (either extrema points or chains) in (weighted) color intensitywithin the neighborhood. The local density refers to the density of theextrema (either extrema points or chains. Specific implementations ofF1(i) and F2(i) are given below as examples. The specific values in theequations below have been empirically chosen to obtain favorable visualresults. Of course, other specific values may be used.

F(i)=F 1(i)×F 2(i)   (Eqn. 1)

F 1(i)=exp [2.5(m _(l) /m _(loc)−1.0)], if m _(i) /m _(loc)>1   (Eqn.2A)

F 1(i)=m _(i) /m _(loc), if m _(i) /m _(loc) 1   (Eqn. 2B)

F 2(i)=(2/π)arctan[-fw(ρ−ρ_(o))]+1,

[0042] where

ρ=ρ_(i)/ρ_(g), and ρ_(o)=0.02×2.2^((2−s))   (Eqn. 3)

[0043] In Equations 2A and 2B, m_(i) is the intensity of the edge pixelat location “i”, and m_(loc) is the local average intensity aroundlocation i. In effect, the value of F1(i) scales up exponentially forhigher intensities (m_(i)/m_(loc)>1) and scales down linearly for lowerintensities (m_(i)/m_(loc)<1).

[0044] In Equation 3, ρ_(i) is the local density at location i, andρ_(g) is the global extrema density. The global density refers to thedensity of extrema (either extrema points or chains) in colorintensities within the image as a whole. The variable ρ₀ represents thecritical density and may be visually and empirically determined. Asindicated by Equation 3, when ρ<ρ₀, F2(i)>1 and otherwise F2(i)<1. “fw”represents a characteristic decay length of F2(i) with respect todensity. F2 is exactly 1 at the reference density and no up-scaling ordown-scaling occurs at this density. As the density deviates from thereference density, F2 also deviates from 1. fw is used to specify howfast F2 deviates from 1 as the density deviates from the referencedensity. A default setting of fw=5 roughly means that we start to seethe significant effect of density deviation when 5*density deviation >1.The variable s is a scale factor. For example, s may be 2, or 3, or 4.Of course, other values for s may also be used.

[0045] In the second distinctive step 304, a dynamic chain-basedthreshold may be determined using the locally-scaled characteristicsdescribed above. In one embodiment, a frame-wide histogram of chainstrengths is determined, where the chain strength are the locally-scaledchain strengths. The dynamic chain-based threshold may then be chosen asa percentage point on the histogram such that a percentage of the chainsare below the threshold.

[0046] The last two steps (206 and 208) are steps common with the secondprocess 200. In the second-to-last step 206, the dynamic chain-basedthreshold is applied to the candidate edge chains. In the last step 208,those candidate edge chains are removed from the set of edge chains thatdo not pass the dynamic chain-based threshold applied in thesecond-to-last step 206. The result is the set of edge chains detectedby this edge-detection process 300 for that image. After that step 208,the process 300 loops back to step 106 and image data is input for anext image.

[0047] In other embodiments, it may be useful to combine globallydynamic chain-based thresholding and locally dynamic chain-basedthresholding in a single image processing technique. This may beaccomplished by adding step 202 to the third process 300 and changingstep 304 such that it determines the dynamic chain-based threshold usingboth global characteristic(s) and locally-scaled strengths.

[0048]FIG. 4 is a diagram depicting a dynamic chain-based thresholdingapparatus 400 in accordance with an embodiment of the invention. Thedynamic chain-based thresholder 400 receives image data. The image datais processed by a candidate edge identifier 402 that identifiescandidate edge chains in an image. The data on candidate edge chain inthe image is input into a dynamic chain-based threshold functioncalculator 404. The calculator 404 determines a dynamic chain-basedthreshold function that is dependent on at least one characteristic ofthe image being processed. Subsequently, a thresholder 406 receives boththe dynamic chain-based threshold function and the data on candidateedge chains in the image. The threshold applicator 406 applies thedynamic chain-based threshold function to the candidate edge chains. Theoutput of the thresholder 400 are the extracted edges from the image.

[0049]FIG. 5 is a diagram depicting a system 500 in accordance with anembodiment of the invention. The system 500 includes both an encoder 502and a decoder 504. The encoder 502 may be, for example, incorporatedinto a video transmitting apparatus. Similarly, the decoder 504 may beincorporated into a video receiving apparatus operable in cooperationwith the encoder 502. The encoder 502 may communicate video informationby way of a communications channel suitable for transmission of digitaldata.

[0050] As shown in FIG. 5, both the encoder 502 and the decoder 504 mayinclude and utilize the dynamic chain-based thresholder 400. This isadvantageous in that, if both encoder and decoder use the samethresholder 400 to extract edges from images, then less informationabout the image needs to be transmitted. This reduces the bandwidthrequired to transmit video images while maintaining a visual qualitylevel. Additional discussion of the advantages of such a system is givenin related U.S. patent application Ser. No. 09/550,705, filed Apr. 17,2000 and entitled “Method and Apparatus for Efficient Video Processing,”and related U.S. patent application Ser. No. 09/591,438, filed Jun. 9,2000 and entitled “Method and Apparatus for Digital Image Segmentation,”the disclosure of both of which are incorporated herein by reference.

[0051] In the above description, numerous specific details are given toprovide a thorough understanding of embodiments of the invention.However, the above description of illustrated embodiments of theinvention is not intended to be exhaustive or to limit the invention tothe precise forms disclosed. One skilled in the relevant art willrecognize that the invention can be practiced without one or more of thespecific details, or with other methods, components, etc. In otherinstances, well-known structures or operations are not shown ordescribed in detail to avoid obscuring aspects of the invention. Whilespecific embodiments of, and examples for, the invention are describedherein for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize.

[0052] These modifications can be made to the invention in light of theabove detailed description. The terms used in the following claimsshould not be construed to limit the invention to the specificembodiments disclosed in the specification and the claims. Rather, thescope of the invention is to be determined by the following claims,which are to be construed in accordance with established doctrines ofclaim interpretation.

What is claimed is:
 1. A method for image processing, the methodcomprising: identifying candidate edge chains in an image beingprocessed; determining a dynamic chain-based threshold function that isdependent on at least one characteristic of the image being processed;applying the dynamic chain-based threshold function to the candidateedge chains; and removing from a set of edge chains those candidate edgechains that fail to pass the dynamic chain-based threshold function. 2.The method of claim 1, wherein the at least one characteristic of theimage comprises a global characteristic of the image.
 3. The method ofclaim 2, wherein the at least one characteristic of the image comprisesa plurality of characteristics of the image.
 4. The method of claim 2,wherein the global characteristic comprises a global measure of colorvariation that is calculated over an image.
 5. The method of claim 4,wherein the global measure comprises a mean measure of the colorvariation.
 6. The method of claim 4, wherein the global measurecomprises a median measure of the color variation.
 7. The method ofclaim 4, wherein the global measure is calculated over the candidateedge chains within the image.
 8. The method of claim 2, wherein thedynamic chain-based threshold function comprises a linear function ofthe global characteristic.
 9. An apparatus for image processing, theapparatus comprising: a candidate edge chain identifier for identifyingcandidate edge chains in an image being processed; means for determininga dynamic chain-based threshold function that is dependent on at leastone characteristic of the image being processed; and a thresholdapplicator for applying the dynamic chain-based threshold function tothe candidate edge chains.
 10. The apparatus of claim 9, wherein the atleast one characteristic of the image comprises a global characteristicof the image.
 11. The apparatus of claim 10, wherein the globalcharacteristic comprises a global measure of color variation that iscalculated over an image.
 12. The apparatus of claim 11, wherein theglobal measure comprises a mean measure of the color variation.
 13. Theapparatus of claim 11, wherein the global measure comprises a medianmeasure of the color variation.
 14. The apparatus of claim 11, whereinthe global measure is calculated over the candidate edge chains withinthe image.
 15. The apparatus of claim 10, wherein the dynamicchain-based threshold function comprises a linear function of the globalcharacteristic.
 16. The apparatus of claim 9, wherein the apparatuscomprises a video encoder.
 17. The apparatus of claim 16, wherein thevideo encoder is configured to operate cooperatively with a videodecoder, and wherein the video decoder also comprises the edgeidentifier, the means for determining, and the thresholder.
 18. Theapparatus of claim 9, wherein the apparatus comprises a video decoder.19. A method for processing an image, the method comprises: determininga dynamic chain-based threshold function that is dependent on at leastone global characteristic of the image being processed; and applying thedynamic chain-based threshold function to a candidate edge chain.
 20. Asystem for image processing, the system comprising: an encoder thatincludes a candidate edge chain identifier for identifying candidateedge chains in an image being processed, means for calculating a dynamicchain-based threshold function that is dependent on at least onecharacteristic of the image being processed, and a threshold applicatorfor applying the dynamic chain-based threshold function to the candidateedge chains; and a decoder configured to operate in cooperation with theencoder, wherein the decoder also includes the candidate edge chain, themeans for, and the threshold applicator.